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@PHDTHESIS{Zou:905046,
      author       = {Zou, Wei},
      title        = {{M}achine {L}earning in {M}odeling of the {D}ynamics of
                      {P}olymer {E}lectrolyte {F}uel {C}ells},
      volume       = {560},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2022-00345},
      isbn         = {978-3-95806-601-4},
      series       = {Schriften des Forschungszentrums Jülich Reihe Energie $\&$
                      Umwelt / Energy $\&$ Environment},
      pages        = {157},
      year         = {2021},
      note         = {Dissertation, RWTH Aachen University, 2021},
      abstract     = {Polymer electrolyte fuel cells (PEFCs) are a promising
                      energy conversion technology thatgenerates electricity from
                      hydrogen with low noise, and less or zero emission
                      properties.Phenomena during the fuel cell operation are
                      complex, which are caused by many interrelatedfactors. In
                      addition, the dynamic behaviors of the fuel cells will
                      change due to differentoperating conditions and load
                      changes. A fast response model that can predict the
                      PEFCsdynamic behavior is helpful to implement optimal
                      control to the fuel cell systems obtaining adesired
                      performance.The aim of the thesis is to developing,
                      analyzing and modifying a fuel cell dynamic model,in which a
                      least squares support vector machine (LSSVM) is employed.
                      The efficiency of theLSSVM model is first demonstrated in
                      comparison to experimental data collected from a fuelcell
                      test rig. Analyzing the model’s performance under various
                      fuel cell load changes is carriedout with the help of
                      experimental data collected from our test rig and artificial
                      data generatedby a white-box model that based on the
                      mechanism of the fuel cell systems. Two types ofartificial
                      data are generated: one is idealized artificial data with
                      determined cell voltage andanother one is oscillated
                      artificial data that includes the oscillation on the cell
                      voltage.Various load changes, namely current density
                      changes, are considered in the analysis, andare represented
                      by a combination of two factors called as ramp time and ramp
                      value. Ramp timeis used to show how fast the load is changed
                      and ramp value is used to describe the range ofload change.
                      In addition, considering the data-driven nature of the LSSVM
                      method, samplinginterval of the test rig that determines the
                      frequency of data collection is considered. It is foundthat
                      the performance of the LSSVM model is better when smoother
                      load changes are imposedon the system, so large ramp time
                      and small ramp value are preferable in order to achieve
                      goodmodel accuracy. Moreover, to modeling a high dynamic
                      fuel cell system, a high frequencysampling is suggested to
                      reach a desirable model performance.The thesis defines a
                      working zone for the LSSVM model when predicting the
                      PEFCsdynamic response to sudden load change. Based on the
                      acceptable error to the modeling, a setof workable
                      combinations of sampling interval, ramp time and ramp value
                      can be found. Theworking zone helps to instruct the future
                      application of the LSSVM model when differentoperating load
                      changes are applied.Last but not the least, the LSSVM model
                      is modified in order to improve its modelingperformance when
                      predicting the dynamic behavior of the fuel cell. An online
                      adaptive LSSVMmodel is developed. Determination of initial
                      value of the internal parameters to the LSSVMmodel is
                      optimized by employing a genetic algorithm to search global
                      optimum instead ofmanual search. An adaptive process is
                      carried out to update these internal parameters online.With
                      a suitable starting point of the internal parameters and
                      online updating processes, thisonline adaptive LSSVM model
                      can well deal with complex nonlinear fuel cell systems
                      withfrequent load changes},
      cin          = {IEK-14},
      cid          = {I:(DE-Juel1)IEK-14-20191129},
      pnm          = {1231 - Electrochemistry for Hydrogen (POF4-123)},
      pid          = {G:(DE-HGF)POF4-1231},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2022020831},
      url          = {https://juser.fz-juelich.de/record/905046},
}